Post-simulation Analysis of Monte Carlo Experiments: Interpreting Pesaran's (1974) Study of Non-nested Hypothesis Test Statistics
Abstract"Monte Carlo experimentation in econometrics helps 'solve' deterministic problems by simulating stochastic analogues in which the analytical unknowns are reformulated as parameters to be estimated." (Hendry (1980) With that in mind, Monte Carlo studies may be divided operationally into three phases: design, simulation, and post-simulation analysis. This paper provides a guide to the last of those three, post-simulation analysis, given the design and simulation of a Monte Carlo study, and uses Pesaran's (1974) study of statistics for testing non-nested hypotheses to illustrate the techniques described. A statistic is derived for testing for significant deviations between the asymptotic and (observed) finite sample properties. Further, that statistic provides the basis for analyzing discrepancies between the finite sample and asymptotic properties using response surfaces. The results for Pesaran's study indicate the value of asymptotic theory in interpreting finite sample properties and certain limitations for doing so. Finally, a method is proposed for adjusting the finite sample sizes of different test statistics so that comparisons of their power may be made. Extensions to other finite sample properties are indicated.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Review of Economic Studies.
Volume (Year): 53 (1986)
Issue (Month): 4 (August)
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Other versions of this item:
- Neil R. Ericsson, 1986. "Post-simulation analysis of Monte Carlo experiments: interpreting Pesaran's (1974) study of non-nested hypothesis test statistics," International Finance Discussion Papers 276, Board of Governors of the Federal Reserve System (U.S.).
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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